In many fields including security technology, there is a need for a system and a method for recognizing faces of individuals with higher accuracy.
A face recognition method using templates and that utilizing features obtained based on values of brightness are conventionally well known. Heisele, B., Poggio, T. and Pontil, M., (2000), “Face detection in still gray images”, A. I. Memo 1687, Center for Biological and Computational Learning, MIT, Cambridge, Mass., a method is proposed in which features of components such as eyes, a nose and a mouth are extracted from an input image under little influence of brightness, for face recognition with higher accuracy. In the method, a support vector machine (SVM) is used to classify the extracted features of components such as eyes, a nose and a mouth, for face recognition.
However, conventional systems or methods require a huge amount of data for face recognition with higher accuracy. For example, preparing templates for various viewpoints increases data without flexibility. Further, learning features of components such as eyes, a nose and a mouth, at different viewpoints and under different conditions of illumination, requires an exponentially increasing amount of information and a volume of databases containing the information, although such a learning will increase accuracy of face recognition.